Recent Patterns in Lake Erie Phosphorus and Chlorophyll a Concentrations in Response to Changing Loads.

Despite the initial success of extensive efforts to reduce phosphorus (P) loading to Lake Erie as a part of the Great Lakes Water Quality Agreement, Lake Erie appears to be undergoing a re-eutrophication and it is plagued by harmful algal blooms. To offer insights into potential lake responses under differing Maumee River loads and reveal recent changes with time, we explored patterns in phosphorus and chlorophyll a data from 2008 to 2018 collected in western Lake Erie near the mouth of the Maumee River. We found high, but relatively stable Maumee River and lake concentrations of total P (TP) and soluble reactive P (SRP) with no discernable annual or seasonal patterns. Maumee spring TP load was not strongly related to lake TP, and lake SRP concentrations were positively but weakly related to SRP loads. Lake TP was a strong predictor of chlorophyll a, but the relationship was weaker at sites closer to the Maumee. These results highlight spatial differences both in P concentration and the relationship between TP and chlorophyll a, and these indicate that spring phosphorus loads are a weak algal biomass predictor in the portion of the western basin of Lake Erie represented by these sampling stations.

Panarchy: opportunities and challenges for ecosystem management.

Addressing unexpected events and uncertainty represents one of the grand challenges of the Anthropocene, yet ecosystem management is constrained by existing policy and laws that were not formulated to deal with today's accelerating rates of environmental change. In many cases, managing for simple regulatory standards has resulted in adverse outcomes, necessitating innovative approaches for dealing with complex social-ecological problems. We highlight a project in the US Great Plains where panarchy - a conceptual framework that emerged from resilience - was implemented at project onset to address the continued inability to halt large-scale transition from grass-to-tree dominance in central North America. We review how panarchy was applied, the initial outcomes and evidence for policy reform, and the opportunities and challenges for which it could serve as a useful model to contrast with traditional ecosystem management approaches.

The distribution and role of functional abundance in cross-scale resilience.

The cross-scale resilience model suggests that system-level ecological resilience emerges from the distribution of species' functions within and across the spatial and temporal scales of a system. It has provided a quantitative method for calculating the resilience of a given system and so has been a valuable contribution to a largely qualitative field. As it is currently laid out, the model accounts for the spatial and temporal scales at which environmental resources and species are present and the functional roles species play but does not inform us about how much resource is present or how much function is provided. In short, it does not account for abundance in the distribution of species and their functional roles within and across the scales of a system. We detail the ways in which we would expect species' abundance to be relevant to the cross-scale resilience model based on the extensive abundance literature in ecology. We also put forward a series of testable hypotheses that would improve our ability to anticipate and quantify how resilience is generated, and how ecosystems will (or will not) buffer recent rapid global changes. This stream of research may provide an improved foundation for the quantitative evaluation of ecological resilience.

Detecting spatial regimes in ecosystems.

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990.

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate-all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS-NE), a collection of preexisting lake data mostly from state agencies. LAGOS-NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year-1 , while TP, TN:TP, and Chl did not change. A minority (7%-16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.

Body size distributions signal a regime shift in a lake ecosystem.

Communities of organisms, from mammals to microorganisms, have discontinuous distributions of body size. This pattern of size structuring is a conservative trait of community organization and is a product of processes that occur at multiple spatial and temporal scales. In this study, we assessed whether body size patterns serve as an indicator of a threshold between alternative regimes. Over the past 7000 years, the biological communities of Foy Lake (Montana, USA) have undergone a major regime shift owing to climate change. We used a palaeoecological record of diatom communities to estimate diatom sizes, and then analysed the discontinuous distribution of organism sizes over time. We used Bayesian classification and regression tree models to determine that all time intervals exhibited aggregations of sizes separated by gaps in the distribution and found a significant change in diatom body size distributions approximately 150 years before the identified ecosystem regime shift. We suggest that discontinuity analysis is a useful addition to the suite of tools for the detection of early warning signals of regime shifts.

Biological invasions, ecological resilience and adaptive governance.

In a world of increasing interconnections in global trade as well as rapid change in climate and land cover, the accelerating introduction and spread of invasive species is a critical concern due to associated negative social and ecological impacts, both real and perceived. Much of the societal response to invasive species to date has been associated with negative economic consequences of invasions. This response has shaped a war-like approach to addressing invasions, one with an agenda of eradications and intense ecological restoration efforts towards prior or more desirable ecological regimes. This trajectory often ignores the concept of ecological resilience and associated approaches of resilience-based governance. We argue that the relationship between ecological resilience and invasive species has been understudied to the detriment of attempts to govern invasions, and that most management actions fail, primarily because they do not incorporate adaptive, learning-based approaches. Invasive species can decrease resilience by reducing the biodiversity that underpins ecological functions and processes, making ecosystems more prone to regime shifts. However, invasions do not always result in a shift to an alternative regime; invasions can also increase resilience by introducing novelty, replacing lost ecological functions or adding redundancy that strengthens already existing structures and processes in an ecosystem. This paper examines the potential impacts of species invasions on the resilience of ecosystems and suggests that resilience-based approaches can inform policy by linking the governance of biological invasions to the negotiation of tradeoffs between ecosystem services.

Implications of Stein's Paradox for Environmental Standard Compliance Assessment.

The implications of Stein's paradox stirred considerable debate in statistical circles when the concept was first introduced in the 1950s. The paradox arises when we are interested in estimating the means of several variables simultaneously. In this situation, the best estimator for an individual mean, the sample average, is no longer the best. Rather, a shrinkage estimator, which shrinks individual sample averages toward the overall average is shown to have improved overall accuracy. Although controversial at the time, the concept of shrinking toward overall average is now widely accepted as a good practice for improving statistical stability and reducing error, not only in simple estimation problems, but also in complicated modeling problems. However, the utility of Stein's insights are not widely recognized in the environmental management community, where mean pollutant concentrations of multiple waters are routinely estimated for management decision-making. In this essay, we introduce Stein's paradox and its modern generalization, the Bayesian hierarchical model, in the context of environmental standard compliance assessment. Using simulated data and nutrient monitoring data from wadeable streams around the Great Lakes, we show that a Bayesian hierarchical model can improve overall estimation accuracy, thereby improving our confidence in the assessment results, especially for standard compliance assessment of waters with small sample sizes.

Long-term and seasonal trend decomposition of Maumee River nutrient inputs to western Lake Erie.

Cyanobacterial blooms in western Lake Erie have recently garnered widespread attention. Current evidence indicates that a major source of the nutrients that fuel these blooms is the Maumee River. We applied a seasonal trend decomposition technique to examine long-term and seasonal changes in Maumee River discharge and nutrient concentrations and loads. Our results indicate similar long-term increases in both regional precipitation and Maumee River discharge (1975-2013), although changes in the seasonal cycles are less pronounced. Total and dissolved phosphorus concentrations declined from the 1970s into the 1990s; since then, total phosphorus concentrations have been relatively stable, while dissolved phosphorus concentrations have increased. However, both total and dissolved phosphorus loads have increased since the 1990s because of the Maumee River discharge increases. Total nitrogen and nitrate concentrations and loads exhibited patterns that were almost the reverse of those of phosphorus, with increases into the 1990s and decreases since then. Seasonal changes in concentrations and loads were also apparent with increases since approximately 1990 in March phosphorus concentrations and loads. These documented changes in phosphorus, nitrogen, and suspended solids likely reflect changing land-use practices. Knowledge of these patterns should facilitate efforts to better manage ongoing eutrophication problems in western Lake Erie.

Discontinuities, cross-scale patterns, and the organization of ecosystems.

Ecological structures and processes occur at specific spatiotemporal scales, and interactions that occur across multiple scales mediate scale-specific (e.g., individual, community, local, or regional) responses to disturbance. Despite the importance of scale, explicitly incorporating a multi-scale perspective into research and management actions remains a challenge. The discontinuity hypothesis provides a fertile avenue for addressing this problem by linking measureable proxies to inherent scales of structure within ecosystems. Here we outline the conceptual framework underlying discontinuities and review the evidence supporting the discontinuity hypothesis in ecological systems. Next we explore the utility of this approach for understanding cross-scale patterns and the organization of ecosystems by describing recent advances for examining nonlinear responses to disturbance and phenomena such as extinctions, invasions, and resilience. To stimulate new research, we present methods for performing discontinuity analysis, detail outstanding knowledge gaps, and discuss potential approaches for addressing these gaps.

Are chlorophyll a-total phosphorus correlations useful for inference and prediction?

Correlations between chlorophyll a and total phosphorus in freshwater ecosystems were first documented in the 1960s and have been used since then to infer phosphorus limitation, build simple models, and develop management targets. Often these correlations are considered indicative of a cause-effect relationship. However, many scientists regard the use of these associations for modeling and inference to be misleading due to their potentially spurious nature. Using data from Saginaw Bay, Lake Huron, we examine the relationship among chlorophyll a, total phosphorus, and algal biomass measurements. We apply graphical models and recently developed "structure learning" principles that use conditional dependencies to help identify causal relationships among observational data. The spurious relationship suspected by some is not supported by our data, whereas a direct relationship between chlorophyll a and total phosphorus is always supported, and an additional indirect relationship with an algal biomass intermediary is plausible under some circumstances. Thus, we conclude that these correlations are useful for simple model building but encourage the use of modern statistical methods to avoid common model-assumption violations.

Do invasive mussels restrict offshore phosphorus transport in Lake Huron?

Dreissenid mussels were first documented in the Laurentian Great Lakes in the late 1980s. Zebra mussels (Dreissena polymorpha) spread quickly into shallow, hard-substrate areas; quagga mussels (Dreissena rostriformis bugensis) spread more slowly and are currently colonizing deep, offshore areas. These mussels occur at high densities, filter large water volumes while feeding on suspended materials, and deposit particulate waste on the lake bottom. This filtering activity has been hypothesized to sequester tributary phosphorus in nearshore regions reducing offshore primary productivity. We used a mass balance model to estimate the phosphorus sedimentation rate in Saginaw Bay, a shallow embayment of Lake Huron, before and after the mussel invasion. Our results indicate that the proportion of tributary phosphorus retained in Saginaw Bay increased from approximately 46-70% when dreissenids appeared, reducing phosphorus export to the main body of Lake Huron. The combined effects of increased phosphorus retention and decreased phosphorus loading have caused an approximate 60% decrease in phosphorus export from Saginaw Bay to Lake Huron. Our results support the hypothesis that the ongoing decline of preyfish and secondary producers including diporeia (Diporeia spp.) in Lake Huron is a bottom-up phenomenon associated with decreased phosphorus availability in the offshore to support primary production.

Seasonal overturn and stratification changes drive deep-water warming in one of Earth's largest lakes.

Most of Earth's fresh surface water is consolidated in just a few of its largest lakes, and because of their unique response to environmental conditions, lakes have been identified as climate change sentinels. While the response of lake surface water temperatures to climate change is well documented from satellite and summer in situ measurements, our understanding of how water temperatures in large lakes are responding at depth is limited, as few large lakes have detailed long-term subsurface observations. We present an analysis of three decades of high frequency (3-hourly and hourly) subsurface water temperature data from Lake Michigan. This unique data set reveals that deep water temperatures are rising in the winter and provides precise measurements of the timing of fall overturn, the point of minimum temperature, and the duration of the winter cooling period. Relationships from the data show a shortened winter season results in higher subsurface temperatures and earlier onset of summer stratification. Shifts in the thermal regimes of large lakes will have profound impacts on the ecosystems of the world's surface freshwater.

Bayesian multilevel discrete interval hazard analysis to predict dichlorodiphenyldichloroethylene mortality in Hyalella azteca based on body residues.

We exposed Hyalella azteca to p,p'-dichlorodiphenyldichloroethylene for intervals of 1 to 4 d and followed mortality out to 10 d. Mortality was determined as the cessation of heartbeat; dead organism body residue was determined daily. To model mortality probability, body residues of the living organisms were estimated using published kinetic data with concentration-dependent rate constants. The estimated residues compared favorably with measured residues in the dead organisms (predicted body residue = 1.302 ± 0.142 measured body residue + 10.351 ± 15.766, r² = 0.64, n = 50). The response data were collected at discrete intervals; thus, it was not possible to determine the exact time of death for organisms. Consequently, we analyzed the mortality data using discrete interval analysis, in a Bayesian hierarchical framework, with body residue as the dose metric. The predicted body residues to produce mortality were similar across the duration of exposure when postexposure mortality was considered. The concentration for 50% mortality was 0.47 µmol/g (148.6 tg/g, range 0.32-0.66 µmol/g), and predictions of response indicted 95% (range 73-99.9%) mortality at 0.79 µmol/g (250 µg/g) and 4% (range 1.2-9.6%) mortality at 0.16 µmol/g (50 µg/g). The lethal residue for 50% mortality based on interval analysis for short-term exposures with postexposure mortality resulted in values similar to long-term continuous exposures for exposure durations of more than 600 h.

Skill Assessment for Coupled Biological/Physical Models of Marine Systems.

Coupled biological/physical models of marine systems serve many purposes including the synthesis of information, hypothesis generation, and as a tool for numerical experimentation. However, marine system models are increasingly used for prediction to support high-stakes decision-making. In such applications it is imperative that a rigorous model skill assessment is conducted so that the model's capabilities are tested and understood. Herein, we review several metrics and approaches useful to evaluate model skill. The definition of skill and the determination of the skill level necessary for a given application is context specific and no single metric is likely to reveal all aspects of model skill. Thus, we recommend the use of several metrics, in concert, to provide a more thorough appraisal. The routine application and presentation of rigorous skill assessment metrics will also serve the broader interests of the modeling community, ultimately resulting in improved forecasting abilities as well as helping us recognize our limitations.

The implications of Simpson's paradox for cross-scale inference among lakes.

Using cross-sectional data for making ecological inference started as a practical means of pooling data to enable meaningful empirical model development. For example, limnologists routinely use sample averages from numerous individual lakes to examine patterns across lakes. The basic assumption behind the use of cross-lake data is often that responses within and across lakes are identical. As data from multiple study units across a wide spatiotemporal scale are increasingly accessible for researchers, an assessment of this assumption is now feasible. In this study, we demonstrate that this assumption is usually unjustified, due largely to a statistical phenomenon known as the Simpson's paradox. Through comparisons of a commonly used empirical model of the effect of nutrients on algal growth developed using several data sets, we discuss the cognitive importance of distinguishing factors affecting lake eutrophication operating at different spatial and temporal scales. Our study proposes the use of the Bayesian hierarchical modeling approach to properly structure the data analysis when data from multiple lakes are employed.

A method to detect discontinuities in census data.

The distribution of pattern across scales has predictive power in the analysis of complex systems. Discontinuity approaches remain a fruitful avenue of research in the quest for quantitative measures of resilience because discontinuity analysis provides an objective means of identifying scales in complex systems and facilitates delineation of hierarchical patterns in processes, structure, and resources. However, current discontinuity methods have been considered too subjective, too complicated and opaque, or have become computationally obsolete; given the ubiquity of discontinuities in ecological and other complex systems, a simple and transparent method for detection is needed. In this study, we present a method to detect discontinuities in census data based on resampling of a neutral model and provide the R code used to run the analyses. This method has the potential for advancing basic and applied ecological research.

A Bayesian approach to retransformation bias in transformed regression.

Ecological data analysis often involves fitting linear or nonlinear equations to data after transforming either the response variable, the right side of the equation, or both, so that the standard suite of regression assumptions are more closely met. However, inference is usually done in the natural metric and it is well known that retransforming back to the original metric provides a biased estimator for the mean of the response variable. For the normal linear model, fit under a log-transformation, correction factors are available to reduce this bias, but these factors may not be generally applicable to all model forms or other transformations. We demonstrate that this problem is handled in a straightforward manner using a Bayesian approach, which is general for linear and nonlinear models and other transformations and model error structures. The Bayesian framework provides a predictive distribution for the response variable so that inference can be made at the mean, or over the entire distribution to incorporate the predictive uncertainty.

The dual role of nitrogen supply in controlling the growth and toxicity of cyanobacterial blooms.

Historically, phosphorus (P) has been considered the primary limiting nutrient for phytoplankton assemblages in freshwater ecosystems. This review, supported by new findings from Lake Erie, highlights recent molecular, laboratory, and field evidence that the growth and toxicity of some non-diazotrophic blooms of cyanobacteria can be controlled by nitrogen (N). Cyanobacteria such as Microcystis possess physiological adaptations that allow them to dominate low-P surface waters, and in temperate lakes, cyanobacterial densities can be controlled by N availability. Beyond total cyanobacterial biomass, N loading has been shown to selectively promote the abundance of Microcystis and Planktothrix strains capable of synthesizing microcystins over strains that do not possess this ability. Among strains of cyanobacteria capable of synthesizing the N-rich microcystins, cellular toxin quotas have been found to depend upon exogenous N supplies. Herein, multi-year observations from western Lake Erie are presented demonstrating that microcystin concentrations peak in parallel with inorganic N, but not orthophosphate, concentrations and are significantly lower (p<0.01) during years of reduced inorganic nitrogen loading and concentrations. Collectively, this information underscores the importance of N as well as P in controlling toxic cyanobacteria blooms. Furthermore, it supports the premise that management actions to reduce P in the absence of concurrent restrictions on N loading may not effectively control the growth and/or toxicity of non-diazotrophic toxic cyanobacteria such as the cosmopolitan, toxin-producing genus, Microcystis.